Результаты динамического моделирования поперечной устойчивости малотоннажного фургона при криволинейном движении

An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques

During the morphing process, the dynamic modeling of the morphing wing aircraft presents the characteristics of rigid‐elastic coupling effects, moving boundaries, and low computational efficiency. Meanwhile, parameters such as aerodynamic forces/moments, center of pressure, center of mass, and moment of inertia would also change significantly, which would seriously affect the flight stability. Therefore, this paper mainly focuses on the rigid‐elastic coupling dynamic modeling of morphing wing aircraft. Based on the virtual power principle, a floating coordinate system and the modal synthesis method are adopted to establish a rigid‐elastic coupling dynamic model that accounts for the elasticity of the wings, which can accurately capture the cross‐scale coupling effects between the rigid‐body motion and the elastic deformation of the wings during the continuously morphing process. Considering the moving boundary problem between the fuselage and the wings during the continuously morphing process, a dynamic constraint modeling technique based on the Craig‐Bampton method and a node's life and death technique describing the constraint states are proposed, which can avoid the complex operations of reestablishing the finite element model of the elastic bodies for traditional methods. Moreover, a method of smoothing model for the rigid–elastic coupling system is developed, which can significantly improve the computational efficiency for the dynamic equation of the morphing wing aircraft. And by changing the value of the smoothing factor, preservation of specific high‐frequency components can be controlled. Finally, the numerical examples are carried out to verify the proposed rigid‐elastic coupling dynamic model for the morphing wing aircraft.

In order to precisely predict the PFC choke inductance and its losses, a dynamic ripple-based inductor modeling technique is proposed considering the real-time current ripple over each switching cycle. The realistic inductor model is synthesized considering the operation temperature, instantaneous current magnitude, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{flux}$</tex> density. Three factors are investigated: (i) the non-linearity of the inductor material; (ii) the switching frequency components in the input current; and (iii) the time variation of the duty cycle. Thus, a function to accurately compute real-time inductance is obtained. To validate the proposed model, simulation studies and experiments are conducted using a single-phase totem-pole PFC rectifier. The theoretical prediction exhibits good agreement with the simulation result with 10% error. It is experimentally verified that the real-time measured inductance matches with the model-predicted inductance at 1 kW output power.

The study introduces three novel strategies for incorporating capabilities for dynamic modelling into multiblock regression methods by integrating sequentially orthogonalised partial least squares (SO‐PLS) with different dynamic modelling techniques. The study evaluates these strategies using synthetic datasets and an industrial example, comparing their performance in predictive ability, identification of process dynamics, and quantification of block contributions. Results suggest that these approaches can effectively model the dynamics with performance comparable to state‐of‐the‐art methods, providing, at the same time, insight into the dynamic order and block contributions. One of the strategies, sequentially orthogonalised dynamic augmented (SODA)–PLS, shows promise by ensuring that redundant information in the time dimension is not included, resulting in simpler and more easily interpretable dynamic models. These multiblock dynamic regression strategies have potential applications for improved process understanding in industrial settings, especially where multiple data sources and inherent time dynamics are present.

Detecting motor bearing faults by stator currents is of great importance as it improves the adaptability of measurement means to different environments and reduces the number of sensors. Therefore, many studies have been conducted to investigate bearing faults by constructing motor models, most of which have used signal models to simulate the dynamics of the bearings. However, the signal model may be exposed to the issue that the nonlinearities in the bearing operation are neglected, thus oversimplifying the coupling effects between the electromagnetic and dynamics models. Hence, a coupled electromagnetic–dynamic modeling method for induction motors based on multiple coupled circuit theory and the rotor-bearing dynamics model is proposed in this study to implement the coupled simulation of electromagnetic and dynamic models. The air gap length and rotor velocity are used as coupled parameters for the calculation of stator–rotor mutual inductance and ball contact deformation, respectively. The simulation results show that the proposed model can effectively implement the electromagnetic–dynamic coupled and reflect the bearing fault characteristics in the current signal. Experiments were conducted on induction motors with typical winding configurations under laboratory conditions. The comparison results verify the effectiveness of the proposed modeling method.

This research investigates the optimization of operational efficiency and cost reduction through the enhancement of material flow management within production line stations. Departing from conventional static analyses, the study employs advanced simulation tools to pinpoint performance bottlenecks and inefficiencies via dynamic modelling techniques. The Ishikawa diagram serves as the primary tool for conducting root-cause analysis. Simultaneously, the 5S methodology is implemented to foster workplace organization, standardization, and hygiene practices. In contrast to traditional optimization frameworks, the proposed strategy integrates real-time performance tracking systems, complemented by adaptive feedback mechanisms. This integration permits ongoing assessment of the production process, facilitating iterative improvement cycles. Empirical data gathered from monitored cycle times, equipment utilization rates, and defect frequencies substantiate the validation of implemented changes. The resulting optimized system significantly minimizes downtime and waste, thereby advancing sustainable and scalable operations. Ultimately, this research demonstrates that the fusion of simulation-based insights with lean management principles leads to considerable improvements in manufacturing productivity and overall product quality.

Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions. The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques. We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively. Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages. No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

For electromagnetic suspension (EMS) type urban Maglev vehicles using a U-shaped electromagnet, the levitation and guidance forces are generated by only one electromagnet. Although the levitation force is actively controlled by changing the voltage of the electromagnet, the guidance force is passively determined by the levitation force. In addition, the curve negotiation performance of EMS-type urban Maglev vehicles using a U-shaped electromagnet must be considered, because an urban guideway may have some curves with shorter radii. It is, therefore, necessary to predict the curving performance with the greatest accuracy possible, in order to improve electromagnetic suspension and establish guideway design specifications. The objective is to establish a new dynamic modelling technique, so as to achieve more realistic curving simulation and thus to more accurately evaluate the curving performance of an EMS-type Maglev vehicle. The use of a full vehicle multibody dynamic model is proposed, and is applied to the evaluation of curving performance. Design changes are also investigated to obtain the bogie design directions for minimising variation in the lateral air gap, which is a criterion for curving performance.

In order to improve the effect of dance teaching action analysis, this paper uses dance action skill feature identification method to analyze dance teaching action and combines teaching and technology to simulate dance action. Moreover, this paper conducts dynamic modeling of dance movements and analyzes the typical properties of several dynamic modeling techniques. At the same time, this paper takes the double-joint dance movement limb as the control object for comparative analysis. The comparison shows that the fractional sliding mode approaching law has better smoothing properties. In addition, this paper selects the control method combining fractional calculus and sliding mode control theory to improve the tracking speed and following effect of the double joints of dance movements. Finally, this paper studies the variance virtual spindle cross-coupling control method based on fractional-order sliding mode to further improve the synchronization accuracy of dance movement limb joints. From the experimental data, it can be seen that the application effect of dance movement skill feature recognition in dance teaching movement analysis is very good.

In field development and reservoir surveillance planning, there is an absolute need to obtain reliable values for reservoir pressures, productivity indexes and other values on a continuous basis so that planning can be done reliably and production / recovery can be optimized. However, continuous data acquisition is often where we stumbled due to a combination of economic, operational/logistical constraints. An alternative is to derive these key parameters from surface data using dynamic well modeling techniques. Subsurface data acquisition will be replaced by surface data acquisition, reducing the number of well interventions and data acquisition costs. The major assumption in current PVT correlations in dynamic well modeling application is instantaneous transformation of one phase (gas) into another phase (oil). During shut-in, this assumption leads to inaccurate modeling of the gas/liquid distribution in the well and hence, an incorrect prediction of interphase level, in-situ density and pressure distribution in the well. The objectives of this research is to improve the current dynamic well modeling technique by incorporating the mass transfer rate model into current PVT model for a reliable well shut-in status description. This is a relatively new and unexplored technical method, which increases the understanding of well behavior. This novel PVT model is able to determine an accurate volume of each fluid phase at any node in the wellbore by incorporating the fluid segregation, mass transfer rate and fluid ingress in the reservoir during shut-in. This better description of wellbore dynamic behavior improves the accuracy of dynamic well modeling ensuring a reliable downhole data determination. This paper presents a status update in well performance modeling and the proposed scheme in developing the novel PVT model. A number of engineering conceptions were reviewed and all the elements that become the foundation of the well bore transient mechanics were discussed.

The application of the direct current (DC) transmission is increasing through the interconnection between grids or the renewable energy resource integration. Various types of DC transmission topology are researched, and the hybrid multi-terminal high voltage DC (HVDC), called the “MTDC”, is one of the research subjects. The hybrid multi-terminal HVDC is the MTDC system that is composed with the Line Commutated Converter (LCC) and Voltage Source Converter (VSC). Most hybrid MTDC research has been focused on the connection of the renewable energy generation sources, especially offshore wind farms. However, the DC grid built with a hybrid MTDC was recently proposed due to the development of the converter technology. Therefore, the DC grid is expected to be able to substitute some parts of the transmission grid instead of the alternating current (AC) system, and the operation strategies of the DC grid are still being researched. The DC grid has the advantage of being able to control the power flow, which can even improve the stability of the connected AC system. The dynamic model is required to analyze the improvement of the AC system by the operation strategy of the hybrid MTDC, however, there is no generic model for the system. In this paper, an operation strategy of the hybrid MTDC is proposed to improve the stability of the AC power system by increasing the utilization of parallel AC transmission lines under the contingency condition. Furthermore, studies on the modeling method for a hybrid MTDC analysis were performed. The proposed modeling method and operation strategy were verified in simulations for which a modified IEEE 39 bus test system was used. The improvement of transient stability by the proposed hybrid MTDC system was shown in the simulation results.

To improve the tank aiming accuracy, this paper studies on the nonlinear vibration characteristics caused by the clearance between the barrel and lining. Based on the parameter identification, a free beam modeling method with clearance is proposed. Firstly, based on the Hamilton principle and hypothesis mode method, the free beam dynamic model framework with clearance is established. Secondly, the clearance time-varying stiffness and damping are obtained by the wavelet identification method from experimental data. Hence, the clearance dynamic model is established to describe the nonlinear dynamic behavior accurately. Finally, based on the similarity theory, the scaled model is used to verify the modeling method effectiveness by evaluating the frequency response function (FRF) error between the proposed model and test model. The results show that the FRF relative error is less than 2% between the measured and simulated value based on time-varying parameters, the coincidence index is more than 85%, under the 95% confidence index. It is proved that the dynamic modeling method could improve the barrel-lining dynamic model accuracy effectively with the clearance factor.

&lt;div class="htmlview paragraph"&gt;Heterogeneous physical systems can often be considered as highly complex, consisting of a large number of subsystems and components, along with the associated interactions and hierarchies amongst them. The simulation of a large-scale, complex system can be computationally expensive and the dynamic interactions may be highly nonlinear. One approach to address these challenges is to increase the computing power or resort to a distributed computing environment. An alternative to improve the simulation computational performance and efficiency is to reduce CPU required time through the application of surrogate models. Surrogate modeling techniques for dynamic simulation models can be developed based on Recurrent Neural Networks (RNN).This study will present a method to improve the overall speed of a multi-physics time-domain simulation of a complex naval system using a surrogate modeling technique. For the purpose of demonstration, a small scale dynamic model of a power system has been developed as a monolithic implementation in Simulink®. The surrogate modeling technique will be evaluated by comparing time dependent responses of the surrogate against the original monolithic with respect to the approximation accuracy and computational performance.&lt;/div&gt;

In this paper, a novel modeling method for hybrid excitation synchronous machines (HESMs) with radial/axial flux paths via magnetic equivalent circuit (MEC) is proposed. 3-D finite-element analysis (FEA) is essential when analyzing a machine with radial/axial flux paths due to the asymmetry in the axial direction. However, 3-D FEA is computationally expensive and time-consuming, especially during the preliminary design stage. The proposed modeling method is analyzed and validated based on an HESM with magnetic shunting rotor. The fundamental structure and working principle of the HESM with magnetic shunting rotor are introduced. On the basis of 3-D structure, an MEC considering saturation and leakage flux distribution is built and illustrated, as well as field analysis is performed. A 2-D equivalent finite-element model via MEC is built and illustrated. A prototype HESM with magnetic shunting rotor has been designed by the proposed modeling method and developed for experimental verification. With the proposed modeling method, the computational efficiency is significantly improved in the design stage. The proposed modeling method is verified by the good agreement between the predicted and measured results.

The rapid growth of healthcare data in recent years calls for more advanced and efficient analytic techniques. Artificial intelligence facilitates finding insightful patterns in massive high-dimensional data. Considering the latest movements towards using machine learning and deep learning techniques in the medical domain, in this study, we focused on the publications in which researchers employed artificial intelligence techniques for cancer diagnosis and treatment. Using dynamic topic modeling and natural language processing techniques, we analyzed the contents and trends of more than 12,000 scientific publications within the period of 2000 to 2018, extracted from two different sources, i.e., Elsevier’s Scopus and PubMed. While drawing the landscape of cancer research, our results also shed light on the evolution of artificial intelligence techniques and algorithms used for cancer diagnosis and treatment. Our findings confirm that modern computer science algorithms are being widely applied to extract patterns from large-scale medical images to cure different types of cancer with a special focus on deep learning techniques in recent years.